Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f3c369552e8>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f3c3687ceb8>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.1.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    real_image = tf.placeholder(tf.float32, [None, image_width, image_height, image_channels], name="real_image")
    z_data = tf.placeholder(tf.float32, [None, z_dim], name="z_data")
    learning_rate = tf.placeholder(tf.float32, name="learning_rate")
    
    return real_image, z_data, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [17]:
alpha = 0.2

def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    #input 28*28*3
    with tf.variable_scope('discriminator', reuse=reuse):
        
        # 14*14*64
        layer1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
        leaky_relu1 = tf.maximum(alpha * layer1, layer1)
    
        # 7*7*128
        layer2 = tf.layers.conv2d(leaky_relu1, 128, 5, strides=2, padding='same')
        batch_normalized2 = tf.layers.batch_normalization(layer2, training=True)
        leaky_relu2 = tf.maximum(alpha * batch_normalized2, batch_normalized2)

        # 4*4*256
        layer3 = tf.layers.conv2d(leaky_relu2, 256, 5, strides=2, padding='same')
        batch_normalized3 = tf.layers.batch_normalization(layer3, training=True)
        leaky_relu3 = tf.maximum(alpha * batch_normalized3, batch_normalized3)

#         # 2*2*1024
#         layer4 = tf.layers.conv2d(leaky_relu3, 1024, 5, strides=2, padding='same')
#         batch_normalized4 = tf.layers.batch_normalization(layer4, training=True)
#         leaky_relu4 = tf.maximum(alpha * batch_normalized4, batch_normalized4)

        flattened = tf.reshape(batch_normalized3, (-1, 4*4*256))
        logits = tf.layers.dense(flattened, 1)
        output = tf.sigmoid(logits)

    return output, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [19]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    with tf.variable_scope('generator', reuse=not is_train):

        layer_to_flatten = tf.layers.dense(z, 4*4*512)

        # 4*4*512
        flattened_layer = tf.reshape(layer_to_flatten, (-1, 4, 4, 512))
        batch_normalized1 = tf.layers.batch_normalization(flattened_layer, training=is_train)
        leaky_relu1 = tf.maximum(alpha * batch_normalized1, batch_normalized1)

        # 7*7*256
        layer2 = tf.layers.conv2d_transpose(leaky_relu1, 256, 4, strides=1, padding='valid')
        batch_normalized2 = tf.layers.batch_normalization(layer2, training=is_train)
        leaky_relu2 = tf.maximum(alpha * batch_normalized2, batch_normalized2)

        #14*14*128
        layer3 = tf.layers.conv2d_transpose(leaky_relu2, 128, 5, strides=2, padding='same')
        batch_normalized3 = tf.layers.batch_normalization(layer3, training=is_train)
        leaky_relu3 = tf.maximum(alpha * batch_normalized3, batch_normalized3)
        
        # Output layer - 28*28*3
        logits = tf.layers.conv2d_transpose(leaky_relu3, out_channel_dim, 5, strides=2, padding='same')
                
        output = tf.tanh(logits)
    
    return output


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [20]:
smooth = 0.1

def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)

    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_logits_real) * (1 - smooth))
    )
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_logits_fake))
    )
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_logits_fake))
    )

    d_loss = d_loss_real + d_loss_fake

    return d_loss, g_loss    

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [21]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    t_vars = tf.trainable_variables()
    
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]    
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)    
        g_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_opt, g_opt    


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [22]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [23]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
#     def model_inputs(image_width, image_height, image_channels, z_dim):
#         """
#         Create the model inputs
#         :param image_width: The input image width
#         :param image_height: The input image height
#         :param image_channels: The number of image channels
#         :param z_dim: The dimension of Z
#         :return: Tuple of (tensor of real input images, tensor of z data, learning rate)

# TODO: Build Model
#    out_channel_dim = 3 if data_image_mode == "RGB" else 1
    
    input_real, input_z, tf_learning_rate = model_inputs(*data_shape[1:], z_dim)

    d_loss, g_loss = model_loss(input_real, input_z, data_shape[-1])

    d_opt, g_opt = model_opt(d_loss, g_loss, tf_learning_rate, beta1)    
    
    step = 0
    display_every = 50
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            print("epoch_i = ", epoch_i)
            for batch_images in get_batches(batch_size):
                step += 1
                
                batch_images = batch_images * 2
                
                # TODO: Train Model
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))

                # Run optimizers
                _ = sess.run(
                    d_opt, feed_dict={input_real: batch_images, input_z: batch_z, tf_learning_rate: learning_rate}
                )
                _ = sess.run(
                    g_opt, feed_dict={input_real: batch_images, input_z: batch_z, tf_learning_rate: learning_rate}
                )
                _ = sess.run(
                    g_opt, feed_dict={input_real: batch_images, input_z: batch_z, tf_learning_rate: learning_rate}
                )
#                 _ = sess.run(
#                     g_opt, feed_dict={input_real: batch_images, input_z: batch_z, tf_learning_rate: learning_rate}
#                 )
                
                if step % display_every == 0:
                    print("step = ", step)
                    d_loss_sample = d_loss.eval({input_real: batch_images, input_z: batch_z})
                    g_loss_sample = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i + 1, epoch_count),
                          "Discriminator Loss: {:.4f}...".format(d_loss_sample),
                          "Generator Loss: {:.4f}".format(g_loss_sample))

                    show_generator_output(sess, 25, input_z, data_shape[-1], data_image_mode)

                
                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [26]:
batch_size = 64
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2


#tf.reset_default_graph()
    
mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
epoch_i =  0
step =  50
Epoch 1/2... Discriminator Loss: 1.7059... Generator Loss: 1.6449
step =  100
Epoch 1/2... Discriminator Loss: 0.9838... Generator Loss: 1.1288
step =  150
Epoch 1/2... Discriminator Loss: 1.6655... Generator Loss: 0.6751
step =  200
Epoch 1/2... Discriminator Loss: 1.4882... Generator Loss: 0.8092
step =  250
Epoch 1/2... Discriminator Loss: 1.4673... Generator Loss: 0.7734
step =  300
Epoch 1/2... Discriminator Loss: 1.7607... Generator Loss: 0.3422
step =  350
Epoch 1/2... Discriminator Loss: 1.2833... Generator Loss: 0.6306
step =  400
Epoch 1/2... Discriminator Loss: 1.5332... Generator Loss: 0.4429
step =  450
Epoch 1/2... Discriminator Loss: 1.4980... Generator Loss: 1.2081
step =  500
Epoch 1/2... Discriminator Loss: 1.3797... Generator Loss: 0.6210
step =  550
Epoch 1/2... Discriminator Loss: 1.4949... Generator Loss: 0.6855
step =  600
Epoch 1/2... Discriminator Loss: 1.4487... Generator Loss: 0.4946
step =  650
Epoch 1/2... Discriminator Loss: 1.4195... Generator Loss: 0.8378
step =  700
Epoch 1/2... Discriminator Loss: 1.4994... Generator Loss: 0.5529
step =  750
Epoch 1/2... Discriminator Loss: 1.3521... Generator Loss: 0.7456
step =  800
Epoch 1/2... Discriminator Loss: 1.3990... Generator Loss: 0.7694
step =  850
Epoch 1/2... Discriminator Loss: 1.5715... Generator Loss: 0.4760
step =  900
Epoch 1/2... Discriminator Loss: 1.6711... Generator Loss: 0.3511
epoch_i =  1
step =  950
Epoch 2/2... Discriminator Loss: 1.4473... Generator Loss: 1.0851
step =  1000
Epoch 2/2... Discriminator Loss: 1.4596... Generator Loss: 0.5758
step =  1050
Epoch 2/2... Discriminator Loss: 1.6903... Generator Loss: 0.3215
step =  1100
Epoch 2/2... Discriminator Loss: 1.3730... Generator Loss: 0.9805
step =  1150
Epoch 2/2... Discriminator Loss: 2.0702... Generator Loss: 0.2275
step =  1200
Epoch 2/2... Discriminator Loss: 1.3645... Generator Loss: 0.6185
step =  1250
Epoch 2/2... Discriminator Loss: 1.3513... Generator Loss: 0.6158
step =  1300
Epoch 2/2... Discriminator Loss: 1.3306... Generator Loss: 0.6301
step =  1350
Epoch 2/2... Discriminator Loss: 3.2022... Generator Loss: 0.0730
step =  1400
Epoch 2/2... Discriminator Loss: 1.1554... Generator Loss: 1.1065
step =  1450
Epoch 2/2... Discriminator Loss: 1.4636... Generator Loss: 0.4854
step =  1500
Epoch 2/2... Discriminator Loss: 1.5706... Generator Loss: 0.3708
step =  1550
Epoch 2/2... Discriminator Loss: 1.5264... Generator Loss: 0.4087
step =  1600
Epoch 2/2... Discriminator Loss: 1.3428... Generator Loss: 0.5826
step =  1650
Epoch 2/2... Discriminator Loss: 1.6025... Generator Loss: 0.3614
step =  1700
Epoch 2/2... Discriminator Loss: 1.2763... Generator Loss: 1.1822
step =  1750
Epoch 2/2... Discriminator Loss: 1.2769... Generator Loss: 0.7991
step =  1800
Epoch 2/2... Discriminator Loss: 1.2909... Generator Loss: 0.8986
step =  1850
Epoch 2/2... Discriminator Loss: 2.6009... Generator Loss: 1.3675

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [25]:
batch_size = 64
z_dim = 100
learning_rate = 0.0005
beta1 = 0.3


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
epoch_i =  0
step =  50
Epoch 1/1... Discriminator Loss: 2.3197... Generator Loss: 1.2102
step =  100
Epoch 1/1... Discriminator Loss: 1.7070... Generator Loss: 0.7253
step =  150
Epoch 1/1... Discriminator Loss: 1.6330... Generator Loss: 0.5249
step =  200
Epoch 1/1... Discriminator Loss: 1.4864... Generator Loss: 0.6797
step =  250
Epoch 1/1... Discriminator Loss: 1.5204... Generator Loss: 0.5858
step =  300
Epoch 1/1... Discriminator Loss: 1.6259... Generator Loss: 0.6011
step =  350
Epoch 1/1... Discriminator Loss: 1.4580... Generator Loss: 0.6990
step =  400
Epoch 1/1... Discriminator Loss: 1.4206... Generator Loss: 0.8073
step =  450
Epoch 1/1... Discriminator Loss: 1.4157... Generator Loss: 0.7266
step =  500
Epoch 1/1... Discriminator Loss: 1.4999... Generator Loss: 0.6567
step =  550
Epoch 1/1... Discriminator Loss: 1.5075... Generator Loss: 0.6208
step =  600
Epoch 1/1... Discriminator Loss: 1.4422... Generator Loss: 0.6886
step =  650
Epoch 1/1... Discriminator Loss: 1.4441... Generator Loss: 0.7423
step =  700
Epoch 1/1... Discriminator Loss: 1.5076... Generator Loss: 0.9328
step =  750
Epoch 1/1... Discriminator Loss: 1.4529... Generator Loss: 0.7091
step =  800
Epoch 1/1... Discriminator Loss: 1.4687... Generator Loss: 0.9272
step =  850
Epoch 1/1... Discriminator Loss: 1.4606... Generator Loss: 0.8416
step =  900
Epoch 1/1... Discriminator Loss: 1.4163... Generator Loss: 0.7409
step =  950
Epoch 1/1... Discriminator Loss: 1.4197... Generator Loss: 0.6931
step =  1000
Epoch 1/1... Discriminator Loss: 1.4322... Generator Loss: 0.6104
step =  1050
Epoch 1/1... Discriminator Loss: 1.4291... Generator Loss: 0.6439
step =  1100
Epoch 1/1... Discriminator Loss: 1.4013... Generator Loss: 0.8847
step =  1150
Epoch 1/1... Discriminator Loss: 1.4718... Generator Loss: 0.7489
step =  1200
Epoch 1/1... Discriminator Loss: 1.4220... Generator Loss: 0.6893
step =  1250
Epoch 1/1... Discriminator Loss: 1.4764... Generator Loss: 0.7155
step =  1300
Epoch 1/1... Discriminator Loss: 1.4154... Generator Loss: 0.7649
step =  1350
Epoch 1/1... Discriminator Loss: 1.4951... Generator Loss: 0.7005
step =  1400
Epoch 1/1... Discriminator Loss: 1.4649... Generator Loss: 0.7227
step =  1450
Epoch 1/1... Discriminator Loss: 1.4733... Generator Loss: 0.8076
step =  1500
Epoch 1/1... Discriminator Loss: 1.5419... Generator Loss: 0.5008
step =  1550
Epoch 1/1... Discriminator Loss: 1.4643... Generator Loss: 0.9450
step =  1600
Epoch 1/1... Discriminator Loss: 1.4024... Generator Loss: 0.7785
step =  1650
Epoch 1/1... Discriminator Loss: 1.4347... Generator Loss: 0.8002
step =  1700
Epoch 1/1... Discriminator Loss: 1.4357... Generator Loss: 0.7217
step =  1750
Epoch 1/1... Discriminator Loss: 1.5016... Generator Loss: 0.6821
step =  1800
Epoch 1/1... Discriminator Loss: 1.3763... Generator Loss: 0.7987
step =  1850
Epoch 1/1... Discriminator Loss: 1.4944... Generator Loss: 0.9519
step =  1900
Epoch 1/1... Discriminator Loss: 1.4681... Generator Loss: 0.7033
step =  1950
Epoch 1/1... Discriminator Loss: 1.4794... Generator Loss: 0.8665
step =  2000
Epoch 1/1... Discriminator Loss: 1.4353... Generator Loss: 0.7947
step =  2050
Epoch 1/1... Discriminator Loss: 1.4255... Generator Loss: 0.7925
step =  2100
Epoch 1/1... Discriminator Loss: 1.3990... Generator Loss: 0.8024
step =  2150
Epoch 1/1... Discriminator Loss: 1.4696... Generator Loss: 0.9003
step =  2200
Epoch 1/1... Discriminator Loss: 1.4795... Generator Loss: 0.8397
step =  2250
Epoch 1/1... Discriminator Loss: 1.4237... Generator Loss: 0.7524
step =  2300
Epoch 1/1... Discriminator Loss: 1.5074... Generator Loss: 0.9192
step =  2350
Epoch 1/1... Discriminator Loss: 1.4843... Generator Loss: 0.6601
step =  2400
Epoch 1/1... Discriminator Loss: 1.4788... Generator Loss: 0.7862
step =  2450
Epoch 1/1... Discriminator Loss: 1.4188... Generator Loss: 0.7884
step =  2500
Epoch 1/1... Discriminator Loss: 1.4306... Generator Loss: 0.9072
step =  2550
Epoch 1/1... Discriminator Loss: 1.4454... Generator Loss: 0.7974
step =  2600
Epoch 1/1... Discriminator Loss: 1.4649... Generator Loss: 0.8885
step =  2650
Epoch 1/1... Discriminator Loss: 1.4579... Generator Loss: 0.8537
step =  2700
Epoch 1/1... Discriminator Loss: 1.4351... Generator Loss: 0.7617
step =  2750
Epoch 1/1... Discriminator Loss: 1.4396... Generator Loss: 0.7874
step =  2800
Epoch 1/1... Discriminator Loss: 1.4279... Generator Loss: 0.7187
step =  2850
Epoch 1/1... Discriminator Loss: 1.4280... Generator Loss: 0.6432
step =  2900
Epoch 1/1... Discriminator Loss: 1.4647... Generator Loss: 0.6568
step =  2950
Epoch 1/1... Discriminator Loss: 1.4074... Generator Loss: 0.7635
step =  3000
Epoch 1/1... Discriminator Loss: 1.3921... Generator Loss: 0.7888
step =  3050
Epoch 1/1... Discriminator Loss: 1.4148... Generator Loss: 0.9488
step =  3100
Epoch 1/1... Discriminator Loss: 1.4131... Generator Loss: 0.7206
step =  3150
Epoch 1/1... Discriminator Loss: 1.4280... Generator Loss: 0.6938

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.